


"""simple docstring"""
import argparse
import json
import os

import torch
from torch import nn

from transformers import NllbMoeConfig, NllbMoeModel
from transformers.modeling_utils import dtype_byte_size
from transformers.utils import WEIGHTS_INDEX_NAME, WEIGHTS_NAME

def 				_snake_case      (   __snake_case							:      Any   ):




   """simple docstring"""







   _lowerCamelCase	:					List[Any]   =		[
       """encoder.version""",
       """decoder.version""",
       """model.encoder.version""",
       """model.decoder.version""",
       """decoder.output_projection.weight""",
       """_float_tensor""",
       """encoder.embed_positions._float_tensor""",
       """decoder.embed_positions._float_tensor""",
   ]
   for k in ignore_keys:
      state_dict.pop(__snake_case						, __snake_case   )

def 				_snake_case      (   __snake_case							:      Dict   ):




   """simple docstring"""







   _lowerCamelCase   ,    _lowerCamelCase	:					Optional[int]   =		emb.weight.shape
   _lowerCamelCase	:					Dict   =		nn.Linear(__snake_case						, __snake_case						, bias=__snake_case   )
   _lowerCamelCase	:					Dict   =		emb.weight.data
   return lin_layer

def 				_snake_case      (   __snake_case							:      Tuple						, __snake_case							:      List[str]=None   ):




   """simple docstring"""







   _lowerCamelCase	:					Any   =		{}
   for old_key in state_dict.keys():
      _lowerCamelCase	:					Optional[int]   =		old_key
      if "moe_layer.experts." in key:
         if expert_idx is not None:
            _lowerCamelCase	:					Optional[Any]   =		key.replace("""moe_layer.experts.0"""						, F'ffn.experts.expert_{expert_idx}'   )
         else:
            _lowerCamelCase	:					str   =		key.replace("""moe_layer.experts."""						, """ffn.experts.expert_"""   )
      if "gate" in key:
         _lowerCamelCase	:					Tuple   =		key.replace(""".moe_layer.gate.wg"""						, """.ffn.router.classifier"""   )
      if "fc2" and "experts" not in key:
         _lowerCamelCase	:					Any   =		key.replace(""".fc2."""						, """.ffn.fc2."""   )
      if "fc1" and "experts" not in key:
         _lowerCamelCase	:					Tuple   =		key.replace(""".fc1."""						, """.ffn.fc1."""   )
      if ".encoder_attn." in key:
         _lowerCamelCase	:					str   =		key.replace(""".encoder_attn."""						, """.cross_attention."""   )
      if "encoder_attn_layer_norm" in key:
         _lowerCamelCase	:					Optional[int]   =		key.replace("""encoder_attn_layer_norm"""						, """cross_attention_layer_norm"""   )
      if "final_layer_norm" in key:
         _lowerCamelCase	:					Any   =		key.replace("""final_layer_norm"""						, """ff_layer_norm"""   )
      _lowerCamelCase	:					Optional[Any]   =		state_dict[old_key]
   return new_dict

def 				_snake_case      (   __snake_case							:      Union[str, Any]						, __snake_case							:      str						, __snake_case							:      List[str]						, __snake_case							:      List[str]						, __snake_case							:      str = WEIGHTS_NAME   ):




   """simple docstring"""







   _lowerCamelCase	:					str   =		[]
   _lowerCamelCase	:					Dict   =		0
   os.makedirs(__snake_case						, exist_ok=__snake_case   )

   for expert in range(__snake_case   ):
      _lowerCamelCase	:					Tuple   =		switch_checkpoint_path + F'-rank-{expert}.pt'
      if os.path.isfile(__snake_case   ):
         _lowerCamelCase	:					int   =		torch.load(__snake_case   )["""model"""]
         remove_ignore_keys_(__snake_case   )
         _lowerCamelCase	:					int   =		rename_fairseq_keys(__snake_case						, __snake_case   )
         _lowerCamelCase	:					Union[str, Any]   =		os.path.join(
             __snake_case						, weights_name.replace(""".bin"""						, F'-{len(__snake_case   )+1:05d}-of-???.bin'   )   )
         torch.save(__snake_case						, __snake_case   )
         sharded_state_dicts.append(expert_state.keys()   )
         total_size += sum([value.numel() for key, value in expert_state.items()]   ) * dtype_byte_size(
             expert_state[list(__snake_case   )[0]].dtype   )

    # Add the last block
   _lowerCamelCase	:					List[Any]   =		os.path.join(__snake_case						, weights_name.replace(""".bin"""						, F'-{len(__snake_case   )+1:05d}-of-???.bin'   )   )
   _lowerCamelCase	:					Dict   =		torch.load(switch_checkpoint_path + """-shared.pt"""   )["""model"""]
   remove_ignore_keys_(__snake_case   )
   _lowerCamelCase	:					Optional[int]   =		rename_fairseq_keys(__snake_case						, __snake_case   )
   _lowerCamelCase	:					List[Any]   =		shared_weights["""decoder.embed_tokens.weight"""]
   sharded_state_dicts.append(shared_weights.keys()   )

   # If we only have the shared weights (dummy model/experts saved on the same file)
   if len(__snake_case   ) == 1:
      _lowerCamelCase	:					Dict   =		os.path.join(__snake_case						, __snake_case   )
      torch.save(__snake_case						, __snake_case   )
      return {weights_name: sharded_state_dicts[0]}, None
   else:
      torch.save(__snake_case						, __snake_case   )
   # Otherwise, let's build the index
   _lowerCamelCase	:					str   =		{}
   for idx, shard in enumerate(__snake_case   ):
      _lowerCamelCase	:					Optional[int]   =		weights_name.replace(""".bin"""						, F'-{idx+1:05d}-of-{len(__snake_case   ):05d}.bin'   )
      _lowerCamelCase	:					List[str]   =		os.path.join(__snake_case						, weights_name.replace(""".bin"""						, F'-{idx+1:05d}-of-???.bin'   )   )
      os.rename(__snake_case						, os.path.join(__snake_case						, __snake_case   )   )
      for key in shard:
         _lowerCamelCase	:					List[str]   =		shard_file

    # Add the metadata
   _lowerCamelCase	:					Optional[int]   =		{"""total_size""": total_size}
   _lowerCamelCase	:					Tuple   =		{"""metadata""": metadata, """weight_map""": weight_map}

   with open(os.path.join(__snake_case						, __snake_case   )						, """w"""						, encoding="""utf-8"""   ) as f:
      _lowerCamelCase	:					Union[str, Any]   =		json.dumps(__snake_case						, indent=2						, sort_keys=__snake_case   ) + """\n"""
      f.write(__snake_case   )

   return metadata, index


if __name__ == "__main__":
 UpperCAmelCase				=						argparse.ArgumentParser()
 # Required parameters
 parser.add_argument(
     """--nllb_moe_checkpoint_path""",
     default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/model_moe_54b/checkpoint_2_300000""",
     type=str,
     required=False,
     help="""Path to a directory containing a folder per layer. Follows the original Google format.""",
 )
 parser.add_argument("""--dtype""", default="""float32""", type=str, required=False, help="""dtype of the saved model""")
 parser.add_argument(
     """--pytorch_dump_folder_path""",
     default="""/home/arthur_huggingface_co/fairseq/weights/checkpoints/hf-converted-moe-54b""",
     type=str,
     required=False,
     help="""Path to the output pytorch model.""",
 )
 UpperCAmelCase				=						parser.parse_args()
 UpperCAmelCase			,  UpperCAmelCase				=						shard_on_the_fly(
     args.nllb_moe_checkpoint_path,
     args.pytorch_dump_folder_path,
     128,
     args.dtype,
 )

 UpperCAmelCase				=						NllbMoeConfig.from_pretrained(
     """facebook/nllb-200-3.3B""", encoder_sparse_step=4, decoder_sparse_step=4, num_experts=128
 )
 config.save_pretrained(args.pytorch_dump_folder_path)
 UpperCAmelCase				=						NllbMoeModel.from_pretrained(args.pytorch_dump_folder_path)
 print("""Done""")
 model.save_pretrained(args.pytorch_dump_folder_path)



"""simple docstring"""



import unittest

from transformers import is_torch_available
from transformers.testing_utils import require_torch


if is_torch_available():
 import torch

 from transformers.generation import DisjunctiveConstraint




@require_torch
class 		a_    (			unittest.TestCase						):







    def   _snake_case       (      self      :							Union[str, Any]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = [[1, 2, 4], [1, 2, 3, 4]]
        _UpperCAmelCase        = DisjunctiveConstraint(__UpperCamelCase  )
        self.assertTrue(isinstance(dc.token_ids  ,       __UpperCamelCase  )  )

        with self.assertRaises(__UpperCamelCase  ):
            DisjunctiveConstraint(torch.LongTensor([[1, 2, 4], [1, 2, 3]]  )  )

        with self.assertRaises(__UpperCamelCase  ):
            DisjunctiveConstraint([torch.LongTensor([1, 2, 4]  ), torch.LongTensor([1, 2, 3, 4, 5]  )]  )







    def   _snake_case       (      self      :							Optional[int]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = [[1, 2], [1, 2, 3, 4]]

        with self.assertRaises(__UpperCamelCase  ):
            DisjunctiveConstraint(__UpperCamelCase  )  # fails here







    def   _snake_case       (      self      :							Optional[int]  )		->Dict:






        '''simple docstring'''






        _UpperCAmelCase        = [[1, 2, 3], [1, 2, 4]]

        _UpperCAmelCase        = DisjunctiveConstraint(__UpperCamelCase  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(1  )
        _UpperCAmelCase        = stepped is True and completed is False and reset is False
        self.assertTrue(__UpperCamelCase  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(2  )
        _UpperCAmelCase        = stepped is True and completed is False and reset is False
        self.assertTrue(__UpperCamelCase  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1, 2]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(3  )
        _UpperCAmelCase        = stepped is True and completed is True and reset is False
        self.assertTrue(__UpperCamelCase  )
        self.assertTrue(dc.completed  )  # Completed!
        self.assertTrue(dc.current_seq == [1, 2, 3]  )







    def   _snake_case       (      self      :							List[Any]  )		->Any:






        '''simple docstring'''






        _UpperCAmelCase        = [[1, 2, 3], [1, 2, 4, 5], [1, 2, 5]]

        _UpperCAmelCase        = DisjunctiveConstraint(__UpperCamelCase  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(1  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(2  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1, 2]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(4  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.current_seq == [1, 2, 4]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(5  )
        self.assertTrue(dc.completed  )  # Completed!
        self.assertTrue(dc.current_seq == [1, 2, 4, 5]  )

        dc.reset()

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(1  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.remaining() == 3  )
        self.assertTrue(dc.current_seq == [1]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(2  )
        self.assertTrue(not dc.completed  )
        self.assertTrue(dc.remaining() == 2  )
        self.assertTrue(dc.current_seq == [1, 2]  )

        _UpperCAmelCase     ,_UpperCAmelCase     ,_UpperCAmelCase        = dc.update(5  )
        self.assertTrue(dc.completed  )  # Completed!
        self.assertTrue(dc.remaining() == 0  )
        self.assertTrue(dc.current_seq == [1, 2, 5]  )